Method and Apparatus for Generation of 3D Models with Applications in Dental Restoration Design

ABSTRACT

Methods and apparatus are provided for generating computer 3D models of an object, by registering two or more scans of physical models of an object. The scans may be 3D scans registered by a curve-based registration process. A method is provided for generating a 3D model of a portion of a patient&#39;s oral anatomy for use in dental restoration design. Also provided are scanning workflows for scanning physical models of an object to obtain a 3D model. A scanner is provided for scanning physical models of a patient&#39;s dentition to obtain scans for generating a 3D model of a patient&#39;s dentition.

RELATED APPLICATIONS

This application is a continuation of, and claims the benefit andpriority to, U.S. patent application Ser. No. 15/455,479, filed Mar. 10,2017, which, in turn, is a continuation of U.S. patent application Ser.No. 14/532,974, filed Nov. 4, 2014, which issued as U.S. Pat. No.9,629,698, which are all hereby incorporated by reference in theirentireties.

BACKGROUND

Methods for registering images of a physical model of an object to forma computer generated three-dimensional model (3D model) are known.Methods for registering multiple scans to make a more complete 3D modelof an object may be time consuming due to the manipulation of large datasets and the manual input required by the user to register two or morescans. For example, in current methods, a user visually identifies andmarks multiple locations on each image to facilitate registration.

3D models of a patient's anatomy are used in Computer Aided Design (CAD)and Computer Aided Manufacturing (CAM) in the field of dentistry to makea range of products including crowns, veneers, inlays and onlays, fixedbridges, dental implant restorations, and orthodontic appliances. Adental CAD restoration often begins with a 3D computer model of apatient's oral anatomy created from the registration of multiple images.It would be desirable to have a method that automatically registersmultiple images to generate a 3D model for use in dental restorationdesign that overcomes the limitations of current methods.

SUMMARY

A computer generated 3D model may be automatically generated byregistering multiple images of an object with a curve-based registrationprocess according to methods described herein. A method is provided forregistering images of an object by automatically identifyingcharacteristic curves that correspond to features of the object'ssurface that have high curvature, and by aligning the characteristiccurves in each image.

A method for generating a 3D model of a physical object is provided thatcomprises obtaining a first image and a second image of the object, andfor each image, identifying characteristic curves on the surface of theobject. A curve from the first image and a curve from the second imagemay be identified that correspond to the same feature of an object andhave a set of points with corresponding local behavior. A transformationthat aligns a set of points on one curve with a set of point on theother curve, may be applied to register the first and second images,generating a 3D model of the object.

Images in the form of photographs, 2D scans, 3D scans, and the like, maybe registered to generate a 3D model. 3D scans capture the surface ofthe object as sets of points, known as point-clouds. For eachpoint-cloud, characteristic curves may be identified that correspond tothe ridges and valleys on the surface of objects with high curvature. Bymethods described herein, alignment and registration of multiple scansmay be performed more efficiently by the use of characteristic curves,than by a data set capturing the entire object shape.

Characteristic curves define features that are intrinsic to an objectand that are not dependent on the relative position or orientation ofthe object during imaging. In an embodiment, distinctive features of aphysical model of a patient's dentition, such as the shape or ridges ofteeth, gingival curves, or the margin line of a tooth preparation, maybe identified and faithfully captured by characteristic curves. Thecurves may be used to characterize or identify aspects of an object or aphysical model, such as the identification of teeth type, or theidentification, matching, and orientation of the physical model.

The characteristic curves may be sampled and encoded in a manner thatcaptures localized curve behavior. Characteristic curves, encoded asstrings, may be used to identify common features within multiple scansto register the scans and generate a 3D model. In one embodiment, amethod is provided for registering a first scan and a second scan of anobject by identifying characteristic curves on each scan and encodingeach curve by a curve encoding process, as a string. Strings from thecharacteristic curves of the first scan are compared to strings from thecharacteristic curves of the second scan to generate a set of stringalignments. By selecting a string alignment that comprises a string froma curve from each scan, a transformation can be identified that alignssets of points on each curve. The same transformation may be applied toalign the first and second scans to generate a 3D model.

In one embodiment, a 3D model of a portion of patient's dentition inneed of a dental restoration is created for use in designing arestoration by CAD/CAM processes. The 3D model may be generated byregistering scans taken of multiple physical models of a patient'sdentition to render a single 3D model. For example, a physical model ofa patient's jaw in need of the dental restoration, known as thepreparation model, as well as a physical model of the opposing jaw, oneor more preparation dies, and a physical model of the patient's upperand lower jaw in articulation, may all be scanned and registeredtogether by the processes described herein.

In one embodiment, a first scan of the articulated model capturescharacteristic curves of the oral anatomy that are also captured by asecond scan of a physical model of the upper jaw and used to registerthe two scans. The scan of the articulated model also capturescharacteristic curves of the oral anatomy that are captured by a thirdscan of a physical model of the lower jaw, and are used to registerthese two scans. In one embodiment, the characteristic curves of eachscan are identified, and encoded as strings by a curved encodingprocess. The set of strings corresponding to curves of the first scanare compared to the set of strings corresponding to the second scan,forming a first set of string alignments; the sets of stringscorresponding to the curves of the first and third scans are compared,and a second set of string alignments is formed. Each string alignmentcomprises a pair of strings—one string from each scan—that correspond toa curve from each scan. Each string alignment is also associated with atransformation that aligns two sets of points—one set of points fromeach curve that corresponds with a string from the string alignment. Afirst transformation is selected that provides optimal alignment betweenthe curves that correspond to a first pair of string alignments. Asecond transformation is selected that provides optimal alignmentbetween curves corresponding to a second set of string alignments. A 3Dmodel of the object is generated by applying the first transformation toregister the first and second scans, and applying the secondtransformation to register the first and third scans. Optionally, scansof a physical model of one or more tooth preparations, also known aspreparation dies, may be scanned, encoded as curves, and registered tothe scan of the preparation jaw by the same method, forming a portion ofthe 3D model.

A scanner is also provided for scanning more than one physical model ata time as a single scan. In one embodiment, a 3D model may be generatedby two scans. A first scan of an articulated model may be obtained byplacing the articulated model on a first pedestal. A second scan may beobtained by scanning an upper jaw, a lower jaw, and one or more toothpreparation dies on a second pedestal, scanned together as a singlescan. The scanner may further comprise a sensor for detecting a specificpedestal, and directing a particular scanning workflow based on whichpedestal is placed on the scanner.

In some embodiments, cloud architecture is utilized to provideefficiency in storage, search, retrieval, and/or registration based onshape descriptors.

It should be appreciated that such methods and apparatus can be usefulfor many other applications including applications outside the dentaldomain.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a 3D model of a patient's dentition.

FIG. 2a is an illustration of a scan of an articulated model of apatient's upper and lower jaws.

FIG. 2b is an illustration of a scan of model of a patient's jaw and apreparation die.

FIG. 2c is an illustration of a scan of a model of a patient's jaw.

FIG. 3a is an illustration of a scan of an articulated model of apatient's jaw with identified curves.

FIG. 3b is an illustration of a scan of a preparation model of apatient's jaw with identified curves.

FIG. 3c is an illustration of a scan of an opposing model of a patient'sjaw with identified curves.

FIG. 3d is an illustration of a tooth depicting characteristic curves ofa tooth.

FIG. 4 depicts the registration of the scan of the articulated model andthe preparation model of a patient's jaw.

FIG. 5a is an illustration of characteristic curves of an upper andlower jaw in articulation.

FIG. 5b is an illustration of characteristic curves of an upper jaw.

FIG. 5c is an illustration of characteristic curves of a lower jaw.

FIG. 6 shows a set of labels used to encode a curve in an embodiment ofthe present disclosure.

FIG. 7 shows a flow diagram of a method of forming a string for a curve.

FIGS. 8a and 8b depict a process of encoding a characteristic curvesaccording to one embodiment.

FIGS. 9a and 9b illustrate an alignment of two sets of curves accordingto one embodiment.

FIGS. 9c and 9d illustrate two exemplary string alignments according toone method described herein.

FIG. 10 shows a work flow diagram for making a computer generated 3Dmodel by curve-based registration according to one embodiment describedherein.

FIG. 11 is an illustration of a scanner according to one embodiment ofthe method described herein.

FIG. 12a shows a physical model of an articulated upper and lower jaw ona pedestal.

FIG. 12b shows physical models of an upper and lower jaw and apreparation die on a pedestal.

FIG. 13 shows a computing system and network connection according to oneembodiment.

While the above-identified drawings set forth presently disclosedembodiments, other embodiments are also contemplated, as noted in thedetailed description. This disclosure presents illustrative embodimentsby way of representation and not limitation. Numerous othermodifications and embodiments can be devised by those skilled in the artwhich fall within the scope and spirit of the principles of the presentdisclosure.

DETAILED DESCRIPTION

Exemplary embodiments of methods, systems and apparatus for thegeneration of 3D models, shape analysis, curve encoding, stringalignment, transformation identification and evaluation, registration ofimages, and applications of such methods, including applications todental restoration design by CAD automation, are provided.

A computer generated three-dimensional model (3D model) of an object maybe generated by a curve-based registration process, by which multipleimages of a single model of the object are registered to generate a 3Dmodel. In an alternative embodiment multiple images taken of multiplephysical models of an object may be registered to generate a single 3Dmodel of the object. In each embodiment, characteristic curves of anobject, captured in more than one image, may be used for registration togenerate the 3D model. With reference to FIGS. 1, and 2 a-c, a computer3D model (100) is generated by registering images of a portion of apatient's oral anatomy based on characteristic curves that are presentin more than one image. In one embodiment, images of more than onephysical model (200, 201, 202, and 206) may be registered to form asingle 3D model (100), by characteristic curves.

An exemplary method of generating a 3D model includes the steps ofidentifying characteristic curves (e.g., 301, 302, 303) from a firstimage (FIG. 3a at 300) of an object and characteristic curves (e.g.,311, 312, 313) from a second image (FIG. 3b at 310) of an object,wherein at least a portion of the characteristic curves representfeatures of an object present in both images. The characteristic curvesfrom the first image (300) and the second image (310) each have a set ofpoints with corresponding local behavior. For a given pair of curves—onefrom each image—a transformation is identified that aligns the set ofpoints on each curve. The transformation is applied to obtain aregistration (400) of the first image (300) and the second image (310′)as illustrated in FIG. 4, forming the 3D model.

Images suitable for use in registration are obtained that provideinformation of characteristic features on the surface of an object.Images of an object may be obtained by imaging the object directly, forexample, by scanning the object. Alternatively, images may be obtainedby imaging an impression made of the object, or imaging a physical modelmade from an impression of the object. An image of a portion of apatient's oral anatomy may be obtained by imaging the patient's mouthdirectly through intraoral scanning, or by imaging a physical impressionmade by traditional impression-making processes used in dentalrestoration. Physical models of a patient's oral anatomy may comprise aphysical impression taken of a person's mouth, for example, with trays.Physical models may also comprise a negative impression that has beencast from a physical impression, such as a stone, plaster, or polymericmodel. Physical models may further include rapid prototype models madefor example, by 3D printing.

Imaging technologies and products are currently commercially availablefor use in scanning impressions and/or physical models of a patient'smouth to create a computer 3D model for use in designing a dentalrestoration. A physical impression or physical model representing apatient's oral anatomy may be scanned directly, for example by atable-top or box scanner. Scanners suitable for use in scanningimpressions or physical models include, for example, optical scanners,such as structured light scanners. Intraoral scanners suitable for usein obtaining impressions directly from a patient's mouth are known andare commercially available. Data obtained from scanning the surface ofan object may be in the form of sets of points, or point clouds, pointtriangles, or meshes. A 3D model may represent an object, for example,by using a collection of points in 3D space connected by variousgeometric entities such as triangles, lines, curves, surfaces and thelike.

By methods described herein, a 3D model (100) of an object may begenerated by registering scans of two or more physical models orimpressions of a patient's oral anatomy. By registering preliminaryscans from two or more models, comprehensive information of the area ofa patient's oral anatomy in need of restoration is obtained. Eachpreliminary scan of a single physical model or impression may beobtained from multiple images taken from one or more known scanpositions. The multiple images of a single model may be assembled into apreliminary scan from a commercially available scanning software programbased on an informed registration process of known positions andorientations through which the scanner or scanning plate moves. The scandata and assembled images may be stored locally, or remotely, forexample, in .stl format, for use in the methods described herein. Thepreliminary scans of each individual object may be saved as a 3D scan,as point clouds or meshes for use in the methods described herein.

The preliminary scans from more than one physical model of a patient'soral anatomy may be assembled by the methods described herein to form acomputer 3D model of the patient's oral anatomy. In one embodiment, thescans of the physical models are preliminary 3D scans that areregistered to generate a 3D model of the patient's oral anatomy. In oneembodiment, scans of the buccal, lingual and occlusal surfaces of theindividual physical models of the upper jaw, lower jaw and toothpreparation die provide a large data set with comprehensive informationthat is useful in designing a restoration. However, for purposes ofregistration, a scan of only the buccal surface of an articulated modelmay provide sufficient data to register the scans of the lower jaw,upper jaw and preparation dies thus, requiring a smaller data set. Asillustrated in FIG. 2a , a preliminary 3D scan of an articulatedphysical model of a preparation jaw (203), a tooth preparation (204) andan opposing jaw (205), is obtained of the buccal surface of thearticulated model. Preliminary 3D scans of the physical models of atooth preparation (206) and preparation jaw (207) exemplified in FIG. 2b, and the opposing jaw (208) illustrated in FIG. 2c , provide scan dataof the buccal (209), lingual (210) and occlusal (211) surfaces. Thesepreliminary 3D scans are registered to generate the computer 3D model ofa patient's oral anatomy illustrated in FIG. 1.

In an embodiment, a method for generating a 3D model of an object bycurve-based registration comprises the steps of identifying a first andsecond set of characteristic curves corresponding to identifyingfeatures of an object that are present in a first scan and a second scanof the object; encoding the curves in each set by a curve encodingprocess, as strings; generating string alignments by comparing thestrings from the first set to the strings of the second set; selecting astring alignment comprising a string corresponding to a curve from eachset of curves; identifying a transformation that aligns a set of pointson each curve corresponding to the string alignment; and registering thefirst and second scans by applying the transformation to the scans toform the 3D model, according to the methods set forth herein.

In accordance with the methods described herein, characteristic curvesmay be used to capture aspects of a shape of an object and provide asimplified representation of the shape. For each scan of an object, aset of characteristic curves can be identified on the point cloud thatcorresponds to areas of high curvature, such as ridges and valleys, onthe surface of an object. Characteristic curves tend to follow paths onthe surface of an object with relatively high curvature, and they arecharacteristic because they are intrinsic to the object and are notdependent upon the relative position or orientation of the object or thescanner.

FIG. 3a exemplifies a scan of a model of a patient's upper jaw (305) andlower jaw (306) in articulation in which characteristic curves have beenidentified (e.g., 301-304) that provide information regarding thealignment of the upper and lower jaws in articulation. The scan of thearticulated physical model of the jaws is used to register scan data ofthe non-articulated physical models of the upper jaw (FIG. 3b at 310)and the lower jaw (FIG. 3c at 320) to form a 3D model. Characteristiccurves identified on the scans of the upper jaw (311, 312, 313) andlower jaw (321, 322, 323) are matched with corresponding curves on thescan of the articulated model to identify the location of each jaw, andprovide an alignment and transformation which may be used to registerthe scans. FIG. 3d exemplifies characteristic curves of a tooth (330)which individually, or as a set, may provide information such as theidentity, position or overall shape of a tooth, or orientation of thejaw in which it is located. Characteristic curves of a tooth (330) mayinclude a margin lines (331), grooves, such as central (332),distobuccal (333), mesiobuccal (334), and lingual (335) developmentalgrooves, and ridges including distal marginal (336), distobuccal cusp(337), mesiobuccal cusp (338), mesiolingual cusp (339), and distolingualcusp (340) ridges.

Curve extracting algorithms are known and may be executed bycommercially available software (such as Geomagic Wrap® offered by 3DSystems) suitable for use in identifying and extracting characteristiccurves from the images obtained by the methods provided herein, as wellas other algorithms, for example as provided in the paper “FeatureExtraction From Point Clouds”, Stephan Gumhold, Xinlong Wang, RobMacLeod (10^(th) International Meshing Roundtable, Sandia NationalLaboratories, October 2011, pp. 293-305). While many curves may beidentified on a model, not all curves characteristic. Many curves lackdescriptive information, and not all curves are necessary for themethods described herein. By identifying parameters to reduce the amountof curve information collected, smaller data sets of curves may becollected for each scan. For example, curves may be eliminated that areon or beyond the gingival region of a model, to provide fewer curves anda smaller data set for later analysis. Once parameters are establishedwhich provide for a sufficiently large enough number of curves for themethods described herein, curves may be identified as illustrated inFIGS. 3a -3 c.

As illustrated in FIGS. 5a-5c , a set of curves for each physical modelis used in the alignment and transformation processes described in themethods provided herein. FIG. 5a depicts a set of characteristic curves(501) that have been extracted from the point cloud of a physical modelof a patient's upper and lower jaw in articulation illustrated in FIG.3a . FIG. 5b depicts a set of characteristic curves (502) of thephysical model of a patient's upper jaw having a tooth preparation,after removal of the surface image shown in FIG. 3b . FIG. 5c depictsthe set of characteristic curves (503) of the physical model of apatient's slower jaw, or the opposing jaw that is opposing thepreparation jaw, after removal of the surface image shown in FIG. 3c .Characteristic curves extracted as a data set from each scan may beencoded as strings for efficient comparison with strings from curves ofother scans to determine proper scan alignment for registration methods.

A curve encoding process is described to encode the curve based on thelocal behavior of sequentially sampled points. One method for encodingcurves is described in commonly owned US Patent Application Publication2014/0278279, which is incorporated herein in its entirety. As describedtherein, the behavior of the curve may be symbolically represented by aset of labels, for example by an alphabet letter, wherein each letterrepresents certain behavior at sequential sample points on thediscretized curve. In one embodiment, a sequence of points on the curveis selected so that the arc length between consecutive sample points isa fixed step size.

The curve may be sampled at any density that is suitable for detectinglocalized behavior that identifies distinct characterization of theoverall shape of the object to be encoded. For example, in oneembodiment a step size in the range of from about 0.1-1 mm, such asabout 0.5 mm, may be suitable for detecting characteristic features of apatient's dentition. If the density or sample distance is too great ortoo small for a given curve, the specific curve behavior thatcharacterizes or captures an overall shape may not be identified.Additionally, tangent lines or approximate tangent lines may be storedat each sample point. If the curve is represented synthetically (e.g. asa nurb curve) then exact representations of tangent lines at any pointon the curve may be available. If the curve is represented as apolygonal line, then tangent lines may be approximated by the edges.

The method for encoding a characteristic curve comprises associating alabel with sequential sample points based on localized behavior of thecurve in the region of the sample point. As shown in FIG. 6, a set ofbehaviors (600) identified by labels (601, 602, 603, 604, and 605) maybe collectively used to encode the overall behavior of a curve. FIG. 7is a flow diagram of a method of encoding a curve in a set of curves asa string (700), that comprises the steps of identifying and extracting aset of curves from an image of an object (701); sampling each curve inthe set at a constant density (702); assigning a label to each vertexbased on a predefined set of labels (703); and forming a string bylinking the labels for each curve (704).

For each curve in each curve set, the sequence of sample points in thatcurve is converted into a sequence of labels that are selected from aset of labels that defines a behavior. In one embodiment, the behaviorover a set of four consecutive points on a curve (such as sample points1, 2, 3, and 4) is detected and a label selected from a set of labels,such as A, B, C, D, or, E, representing the behavior may be associatedwith the first point. The behavior of a further set of sampled points(such as sample points 2, 3, 4, and is detected and a label thatidentifies this behavior is associated with the next sample point. Themethod of detecting behavior and associating a label for that behaviormay continue similarly for the remainder of the curve. Encoding may beperformed in both directions along a curve, and a string of labels foreach direction may be identified.

The flow diagram of FIG. 8a , and FIG. 8b , provides one example of amethod for detecting the localized behavior of a curve and associating alabel based on that behavior, as exemplified by the labels in FIG. 6.Computer-executable instructions may be provided by which angles of acurve are computed (800) to determine the localized behavior (801). Inone embodiment, the letter A (804) is associated with a sample pointwhere localized behavior of the curve is substantially smooth, andangles (alpha (811) and beta (812)) are below a defined threshold (802,803). Likewise, B (805) is associated with a sample point where theangle of a curve (alpha) is below a defined threshold (802) for aportion of the sample region, and the angle beta (803) is above athreshold, having a turn, in another portion of the curve. The letter C(807) depicts localized behavior of curve having a first turn or angle(alpha) greater than a defined threshold, followed by no detectableangel or turn (806). The letter D (809) may be used to represent an areaof the curve over a set of sample points wherein the localized behaviorcomprises a first detectable angle or turn in a first direction, and asecond detectable angle or turn in second direction that is differentfrom the first (808); and the letter E (810) may be used to representlocalized behavior having a first detectable angel or turn in a firstdirection, and a second angle or turn in a similar direction.

FIG. 8b depicts behavior calculations for angles alpha (813) and beta(814). Linked together, the labels depicted in FIG. 6 constitute a chaincode that may be represented as a string, stored in a searchable format,and linked to a file comprising information about the curve and the scandata. One skilled in the art would understand that other labels could besubstituted for alphabetic labels of FIG. 6. Further, other methods ofanalyzing the curves and behaviors other than the behaviors describedand exemplified in FIGS. 6 (601, 602, 603, 604, and 605), and FIGS. 8aand 8b , may be used to characterize localized behavior of the curve.

In one embodiment, local behavior is defined by curvature and torsion,and a set of labels is provided, wherein each label represents adifferent local curvature and/or torsion. In one method, a sequenceconsisting of 3 or fewer sample points is encoded by the empty string.The sequence p₀, p₁, p₂, . . . p_(n-1) with n>3 is encoded using a fixedparameter epsilon chosen in the interval (0,1]. For i=0, 1, 2, . . . ,n−4, the ‘ith character s_(i), in the code is chosen from the set {A’,B′, C′, D′, E′} as follows:

Let e_(i) denote the square of the sine of the angle at p_(i) of the“elbow” (p_(i−1), p₁, p_(i+1)).

Case: e_(i+1)<ε and e_(i+2)<ε

s_(i): =A′

Case: e_(i+1)>=ε and e_(i+2)<ε

s_(i): =B′

Case: e_(e) _(i+i) <ε and e_(i+2)>=ε

s_(i): =C′

Case: e_(e) _(i+1) >=ε and e_(i+2)>=ε

In this case, the triple e_(i), e_(e) _(i+1) and e_(i+2) determine anoriented plane. If e_(i)+3 lies above this plane, then s_(i):=D′,

otherwise, s_(i):=E′.

As above, sample points on a curve are denoted with labels linkedtogether to form a chain code stored as a string.

Computer executable code or programs for use in the encoding process maybe provided for, example in .NET, or C++. In one embodiment, a methodcomprises providing computer executable instructions comprising rules orcode for sampling a curve, detecting the behavior of a plurality of setsof points on the curve, associating the behavior of a set of points witha label, and linking together labels to form a chain code for each curvein the curve set. The chain code for each curve may be represented as astring, and each scan is associated with a set of strings thatcorrespond with the set of characteristic curves that compactly identifyfeatures of the scan for registration.

Numerous string alignments are formed for the two point clouds to beregistered. String alignments are formed by pairing each string from oneset of strings representing a first point cloud and each string fromanother set of strings representing a second point cloud. A set ofstring alignments includes the string alignments formed between twopoint clouds. Because a string is an ordered tuple of labels thatrepresents the behavior of a curve, a string alignment determines acorrespondence between sets of points on the two curves correspondingwith the pair of strings (one curve from each curve set). Thecorrespondence between some labels of each string reflects thesimilarity of the behavior of the curves the strings represent.Parameters for generating string alignments for the curve sets of twoscans may be established, for example, by the selection of a minimum ormaximum number of sample points on a curve to be encoded as a string,the selection of the number of matching labels in a string alignment,and the like. The set of string alignments may be filtered to reduce theset size, for example, by eliminating alignment pairs that have lowcorrespondence of labels.

Each string alignment in the set of string alignments for a set ofcurves determines a possible Euclidean transformation to align sets ofpoints on each curve that corresponds to the string alignment.

FIG. 9a illustrates a registration of two sets of characteristic curves,(501) of FIG. 5a seen as (901) and (502) of FIG. 5b seen as (902) thathave been automatically aligned by the methods described herein. Onestring alignment corresponding to a pair of curves, or a subset of acurve, (907 and 908) may be automatically selected from among a set ofstring alignments for determining a transformation suitable for aligningthe curve sets. A multiplicity of pairs of curves is illustrated (903and 904; 905 and 906; 907 and 908) that align closely based on thetransformation identified for curves (907 and 908). FIGS. 9b and 9cillustrate a string alignment (909) of one pair of curves (910). Thebehavior of two curves (907 and 908) over a sequence of points have beenencoded as strings by labels A, B, C, D, or E (e.g. 912 and 912′; 913and 913′; 914 and 914′; 915 and 916). The correspondence of labels ontwo strings, or string subsets, reflects the correspondence of behaviorof two curves over the set of points. Where the behavior of two curvesis similar, the sets of labels for given sets of corresponding pointsfor each curve will be similar. Where the behaviors of two curvesdiffer, the labels for given sets of corresponding points of a string ofa string alignment differ (915 and 916). Curve lines that appeardisconnected from closer curves in a curve set, as seen in FIG. 9a , mayalso be evaluated as part of a string set. FIG. 9d illustrates a pair ofcurves (920 and 921) and strings of labels indicating correspondence ofa set of points on a curve (e.g., 923 and 923′; 924 and 924′), andlabels that show a lack of correspondence of a set of points on a curve(e.g., 924 and 925; 926 and 927).

After generating string alignments, a transformation is identified foreach alignment that attempts to align a set of points on one curve witha set of points on a second curve, for curves corresponding to the pairof strings of the alignment. A transformation that identifies therotation and/or translation required to register the sets of points ontwo curves to bring the curves into alignment may also be applied toregister the two scans that correspond with the curves, as seen inFIG. 1. Thus, a plurality of string alignments provides a plurality ofpossible transformations that approximate registration. Transformationsfor each string alignment may be ordered to select the besttransformation for registering two scans.

In one embodiment, a set of transformations generated from the set ofstring alignments are evaluated to determine the distance between thecurves corresponding to the strings. In one embodiment, the distancebetween curves is measured by a proximity count, or measurement. Thetransformations may be ordered by a proximity measurement to select acandidate transformation for registering the scans. In one embodiment,proximity may be calculated for each transformation by measuring theaverage number of points from one curve that are within a designateddistance from points on another curve. Other measures of proximity maybe used, as well as other methods for evaluating and selecting whichtransformation to use to register two scans. From a possible set oftransformations that may be ordered based on proximity, onetransformation may be selected to register two point clouds.

In FIG. 10, a workflow diagram depicts one method of generating a 3Dmodel for use in making a dental restoration for a patient. The methodcomprises the steps of obtaining scan data of a portion of a surface ofa patient's dentition in need of restoration (1001), and forming pointclouds representing the scanned surface (1002). For example, scans oftwo or more impressions or physical models of a patient's oral anatomymay be obtained. The method further comprises identifying a set ofcharacteristic curves from each set of scan data representing each model(1003). Each set of characteristic curves is encoded by a curve encodingprocess (1004), the curve encoding process comprising the steps of: i)sampling points along a characteristic curve at a constant density; ii)identifying local behavior over a set of adjacent sample points on acurve; iii) assigning a label to a sample point that identifies thebehavior of a set of adjacent points; and iv) linking the labelstogether to form a string for each characteristic curve to form a stringset corresponding to each point cloud. The method further comprisesgenerating a set of string alignments by pairing each string from onestring set to each string from another string set (1005). The methodfurther comprises obtaining a transformation for each string alignment(1006) that aligns a set of points on each curve corresponding to eachstring of a string alignment. The set of transformations are evaluated(1006) by the processes described herein, such as by proximity, andordered. A transformation is selected, and applied to register two ofthe scans (1008) and generating a 3D model from the registered scans(1009). The process may be repeated for each pair of scans to beregistered.

Advantageously, by the methods described herein, a 3-D model of apatient's oral anatomy may be generated by registering one or more scanscomprising: a physical model of an upper jaw, a physical model of alower jaw, an articulated physical model of the upper and lower jaws,and a physical model of a tooth preparation in the form of a preparationdie. In one embodiment, scans of an articulated model and an upper jaware first registered by the method described herein, and scans of thearticulated model and the lower jaw are registered. In a furtherembodiment, scans of one or more preparation dies and the preparationjaw (e.g. the upper or lower jaw, or both) are registered before orafter the registration of the scans of the jaws and the articulatedmodel. The scans may be provided as 3D scans, 2D scans, and/or scan datain the form of point clouds, meshes, and the like, and registeredaccording to the methods described herein, for example as provided inthe workflow of FIG. 10a as described above.

In one embodiment, scan data from an articulated physical model of anupper and lower jaw may be used to provide the relative position of theupper and lower jaws in articulation. The articulated model may bescanned at a lower resolution, or scanned from only one surface, thus,providing a computer file with less data and fewer curves for analysisby the methods described herein. In one embodiment, scans of thephysical models of the upper and lower jaws are registered to thearticulated model, and after registration, scan data of the articulatedmodel is removed from the registration. The 3D model of the patient'soral anatomy is generated that comprises scan data of the upper jaw andthe lower jaw in registration, oriented as provided by the articulatedmodel, without inclusion of the scan data of the articulated model inthe final 3D model.

One embodiment of scanner suitable for use in scanning an object togenerate a 3D model according to the described methods, is exemplifiedin FIG. 11. In this embodiment, the scanner (1100) comprises a base(1101) for directly or indirectly supporting the components of thescanner. A support arm (1102) is attached at a first end (1103) to afirst portion of the base (1101). The scanner further comprises ascanner head assembly (1104) comprising a scanning system (1105) forscanning a physical object, that is attached to a second end (1106) ofthe support arm (1102). The scanning system (1105) may comprise a laserilluminating device and a light detecting device. In one embodiment, thescanning system may be a structured light scanner.

The scanner (1100) further comprises a swing arm assembly (1107) that ispivotally attached to the support arm (1102) at a first end for movingthe swing arm in both directions along a first axis (‘B axis’) (1008)for changing the angle of the mounting or scanning surface of thepedestal during scanning. The total range of motion along the B axis inboth directions may be from about 80 degrees to about 120 degrees fromthe swing arm resting position where the pedestal top surface ishorizontal. The swing arm assembly comprises a second end (1109) thatcomprises a turntable (1110). The turntable provides rotation of apedestal (1111) around a second axis (‘A’ axis), and optionally,rotatable in both directions on an axis of rotation. Thus, in oneembodiment, a scanner is provided having a base (1101), a support arm(1102) attached to the base at a first end and, and further attached tothe scanner head assembly (1104) and swing arm assembly (1107) whereinthe swing arm provides displacement of a pedestal (1111) or an a objectin two directions (an A axis and a B axis). In one embodiment, thesupport arm is attached to the base at only a first end, and is notattached to any other portion of the base.

In one embodiment, at least one portion of the swing arm assembly (1107)comprises a bottom surface (1112) that is configured to be parallel andimmediately adjacent a surface on which the scanner is placed. In thisembodiment, the bottom surface (1112) is unbounded by structuralelements of the scanner, for example without a portion of the base or aseparate enclosure, extending beneath the bottom surface (1107) of theswing arm assembly.

The pedestal (1111) may be removably attached to the turntable. The sizeof the pedestal is not fixed, and more than one pedestal may beconfigured to fit the turntable to accommodate small and large objects.In one embodiment, the scanner comprises a pedestal that simultaneouslyaccommodates multiple objects such as a physical model of a preparationjaw, an opposing jaw, and one or more preparation dies, for simultaneousscanning through a scanning workflow to form a single scan. In oneembodiment, placement of the physical models on a single pedestal forscanning may be random. In one embodiment, the scanner comprises apedestal for scanning large articulated models of a quadrant or aphysical model of a full upper and lower jaw either separately or inarticulation.

The selection of a scanning workflow, such as the number of views,rotation of the turntable and scanning angle, may be dependent upon thesize of an object to be scanned. A scanning workflow may beautomatically commenced based on the identity of a pedestal. Where alarger object on a large pedestal may require a different scanningworkflow than smaller objects on a smaller pedestal, separate workflowsappropriate for each may be directed based on the identity of thepedestal. For example, where movement of a pedestal through the B axismay disturb the articulation position of an upper and lower physicalmodel, it may be desirable to have a scanning workflow with minimalchange in the angle of the pedestal from the rest position.

A sensor may be provided that detects the placement of a pedestal ontothe turntable. In this manner, a signal may be communicated to direct ascanning workflow that corresponds with a particular pedestal. In oneembodiment, a magnetic sensing device is provided having a magneticread-switch located, for example, on the turntable, that is activated bya magnet located on the pedestal. Upon placement of the pedestal on theturntable, the magnet communicates a signal to the magnetic-read switchdirecting a specific scanning workflow for the pedestal. In anotherembodiment, the sensing device may comprise a mechanical sensor, such asa micro-switch. In this embodiment, a small mechanical switch or featureon the pedestal depresses a button located, for example, on theturntable, when the pedestal is placed on the turntable, sending asignal to a computing system to activate a scanning work flow for thespecific pedestal.

In another embodiment, the scanner and pedestal comprises an RFID(radio-frequency identification) device. For example, an RFID chip maybe located in the pedestal, and an RFID pick-up is located on thescanner (e.g., on the turntable). A current provided by an RFID chiplocated on the pedestal communicates to the scanner via the RFID pick-updevice, generating a signal that provides information to direct ascanning workflow that is dependent upon the selection of pedestalplaced on the turntable. The RFID device may be an active RFID, and theRFID chip located on the pedestal may communicate with the RFID pick-uplocated, for example on the support arm, the base, swing arm orturntable. The RFID device may be a passive device, requiring closercontact between the RFID chip and RFID pick-up to provide informationabout the pedestal to the computing or scanning system.

In another embodiment, a magnet may be provided as a mechanical stopthat directs a user through a manual scanning workflow. In oneembodiment, a magnet provided on the swing arm interacts with multiplemagnets provided on the pedestal, locking the pedestal into one ofseveral scanning positions during the scanning workflow. Each magnet onthe pedestal provides a locking position when interacting with themagnet on the scanner. To obtain a first scan, for example, a firstmagnet provided on the pedestal locks the pedestal in a first positionupon interaction with the magnet on the swing arm. Upon manual rotationby a user, the contact is broken between the swing arm magnet and thefirst magnet on the pedestal, and a second magnet on the pedestalinteracts with the magnet on the swing arm locking the pedestal in asecond position to obtain a second scan.

In one embodiment, a method is provided for generating a 3D model of aportion of a patient's dentition from only two scans, a first scan of anarticulated model of a patient's upper and lower jaw, and a second scanof the physical models of the upper jaw, the lower jaw and at least onepreparation die. In one embodiment, the method comprises scanning aphysical model of an upper jaw and a lower jaw positioned inarticulation that is placed on a pedestal (for example, as shown at 1200in FIG. 12a ), to obtain a first scan comprising a point cloud of thephysical model. In this embodiment, the orientation of the articulatedphysical model is positioned on the pedestal (1201) in a knownorientation providing information to identify the upper jaw (1202) andlower jaw (1203) of the model (1200). For example, the lower portion ofthe articulated model is placed on the scanning surface (1204) of thepedestal and the upper portion of the articulated model is positionedupwardly. By identifying the orientation of the articulated model, andtherefore identifying the upper and lower portions of the articulatedmodel, the images of the physical models of the second scan may beidentified based on their registration to the articulated model, andlabeled for viewing by a user on an output device such as a displaymonitor. In this embodiment, placement of the physical objects on thepedestal for scanning may be random, since identification of eachphysical model of the second scan is based on registration with thearticulated model. In another embodiment, the preparation die isidentified by registration with the preparation jaw. In one embodiment,the physical model of the preparation die is identified as correspondingto a point cloud that is smaller than either point cloud for the upperjaw or lower jaw.

Several scanning workflows are provided for generating a 3D model fromonly two scans of the physical models. A first scan is obtained of thearticulated physical models of an upper jaw and lower jaw. A second scanis obtained of physical models of the upper jaw, lower jaw, and the atleast one preparation die. In each embodiment, at least one of the upperjaw and the lower jaw in the first and second scans is a physical modelof a preparation jaw, a jaw in which a tooth preparation for a tooth inneed of restoration has been prepared. The jaw opposite the preparationjaw is an opposing jaw (FIG. 12a at 1202). A physical model of thepreparation jaw is prepared as a working model (FIG. 12a at 1203) fromwhich a preparation die (FIG. 12b at 1211) is cut that represents thetooth to be restored. The preparation die is removed from the physicalworking model of the preparation jaw and scanned in the second scan. Byremoving the preparation die from the working model during scanning,features used in registration and design restoration, such as the marginline, may be faithfully captured from the preparation die, duringscanning.

Thus, in one embodiment, a method comprises obtaining a first scan ofthe articulated model of a working model of the preparation jawcomprising the preparation die and an opposing jaw, obtaining scan dataof the buccal, lingual and occlusal surfaces (FIG. 12 a). The methodfurther comprises obtaining a second scan of a set of physical models ofthe working model of the preparation jaw, an opposing jaw, and one ormore tooth preparation dies that have been removed from the workingmodel to obtain a second scan. In this embodiment, scan data of thebuccal, lingual and occlusal surfaces of the working model of thepreparation jaw provide information for registering the preparation dieto the first scan of the articulated model of the working model of thepreparation jaw.

In another embodiment, two physical models of a preparation jaw may bemade. A first model of the preparation jaw may be prepared as a workingmodel in which a preparation die is cut and removed for use in scanningin the second scan. Further, a second preparation jaw may be prepared asa solid model (FIG. 12b at 1212) having a preparation tooth that isintact to provide images of the characteristic curves of the preparationtooth in position in the jaw. In this embodiment, the method comprisesobtaining a first scan of only the buccal surface of the articulatedmodel of a working model of the preparation jaw comprising thepreparation die and an opposing jaw. The method further comprisesobtaining a second scan of the solid model of the preparation jaw, thepreparation die cut from the working model of the preparation jaw, andthe opposing jaw. In this embodiment, the first scan comprising scandata of the buccal surface of the articulated model is used to registerthe preparation jaw and the opposing jaw together. The second scancomprising scan data of the solid model of the preparation jaw is usedto register the scan data of the preparation die to the solid model ofthe preparation jaw.

Scan data of the second scan may be captured as a single point cloud,and a computer implemented method may be used for separating the pointcloud data of the individual physical models of the upper and lowerjaws, and preparation dies, as separate point clouds for use in themethods described herein. Scan of the articulated model containsinformation about the buccal surface of the patient's dentition that isshared with the physical models of the upper and lower jaws in thesecond scan.

The method further comprises identifying a set of characteristic curvesfor each physical model in scans and encoding the characteristic curvesas strings. Encoding comprises the steps of: i) uniformly samplingpoints along the characteristic curves; ii) representing local behaviorover a set of adjacent sample points with a label; and iii) creating astring of labels for each curve. In one embodiment, the margin line ofthe preparation die is identified as a characteristic curve for use inregistering the preparation die obtained.

A multiplicity of sets of string alignments is formed by generating aset of string alignments to between the scan of the articulated modeland each string set of each individual model in the second scan. A setof transformations is identified for each string alignment and evaluatedfor the best transformation to align the scan data of each individualphysical model with the articulated model. A 3D model of the patient'soral anatomy is generated by registering the information from each scan.

In one embodiment, the process further comprises identifying andlabeling each physical model of the second scan based on the orientationof the physical model of the first scan, and the registration of eachphysical model of the second scan to the upper or lower portion of thescan of the articulated model. The identification and labeling of thephysical models of the second scan may be displayed on a user interface,such as a monitor, for verification of the identity of the model by auser.

FIG. 13 exemplifies a computing system that is suitable for use inperforming some or all aspects of the methods according to the flowdiagram of FIG. 10. A computing system (1300) may include one or moredevices such as a scanner, personal computer, lap top, handheld device,or work station, and which may include a central processing unit (CPU)(1301), a system memory (1302), and a system bus (1303) that couples thememory (1302) to the CPU (1301). The computer may also include a storagedevice (1304) for storing one or more programs (1306) and databases(1307). Examples of programs (1306) may include instructions for use incompleting tasks described by modules represented by flow diagrams ofFIG. 10 (i.e., blocks 1001-1009). The memory storage device (1304) andits associated computer-storage media may provide non-volatile storagefor the computing system (1300). In one embodiment, information such aselectronic scan data of physical models, may be stored or obtained froma database that comprises metadata corresponding to the case andassociated with the 3D objects. Metadata may include curve sets, chaincodes, strings, and the number of vertices.

Although the description of the computer-storage media contained hereinrefers to a storage device, such as a hard disk or CD-ROM, it should beappreciated by those skilled in the art that computer-storage media canbe any available storage media that can be accessed by the computingsystem (1300). Computer-storage media may include volatile andnon-volatile, removable, and non-removable media implemented in anymethod or technology for the non-transitory storage of information suchas computer-storage instructions, data structures, program modules, orother data. For example, computer-storage media includes but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, digital versatile disks (DVD), HD-DVD,BLU-RAY, or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage devices, or any other medium which can be used tostore the desired information and which can be accessed by the computingsystem (1300).

In one embodiment, computer-readable medium is provided having storedtherein computer-executable instructions that when executed by acomputing device causes the computing device to perform functions forcarrying out the methods described herein. For example,computer-executable instructions for performing the methods described ineach block of the workflow diagram of FIG. 10 may comprise a module, asegment or a portion of a program code, which includes one or moreinstructions executable by a processor or a computing device forimplementing specific logical functions for carrying out steps in themethods described herein. The instructions may be stored on any type ofcomputer readable medium that is suitable for the computing system usedto carry out the method steps. Method steps and processes described inthe flow diagram of FIG. 10 may be performed locally, using computingsystems comprising programs comprising computer executable instructions,CPU's for executing instructions contained in the programs, and memorysuitable for use in storing electronic files and programs as necessaryfor carrying out the methods described. Alternatively, one or more ofthe programs necessary for performing the methods contained in the flowdiagram of FIG. 10 may be executed in a cloud computing system.

As indicated above, at least a portion of the methods steps describedherein may occur in a cloud computing system. Cloud computing, as usedherein, can refer to computing architectures in which data and programsare shared between one or more computing devices and/or server deviceson a near real-time basis, thus, providing dynamic access or delivery ofdata and/or program modules. Cloud computing system, for purposesherein, may refer generally to a networked computer architecture inwhich at least a portion of the execution of programs or applications,and/or storage of data and software programs, may be provided via acomputer network, in contrast to a local computing system in which bothdata and software are fully contained on a user's computer or computingdevice.

According to various embodiments, the computing system (1300) mayoperate in a networked environment using logical connections to remotecomputers through, for example the network (1310). A computing system(1300) may connect to the network (1310) through a network interfaceunit (1311) connected to the bus (1303). The network interface unit(1311) may connect the computing system to other networks and remotecomputer systems, such as CAD and CAM systems for designing andpreparing a physical restoration based on the 3D model. Thecomputing-system (1300) may also include an input/output controller(1312) for receiving and processing input from a number of input devices(not shown) including a keyboard, a mouse, a microphone and a gamecontroller. Similarly, the input/output controller (1312) may provideoutput to a display or other type of output device. The bus (1303) mayenable the CPU (1301) to read code and/or data to/from the storagedevice (1304) or other computer-storage media.

The program modules (1306) may include software instructions that, whenloaded into the CPU (1301) and executed, cause the computing system(1300) to perform at least some of the steps of the work flow diagram ofFIG. 10, in a cloud computing system. The program modules (1306) mayalso provide tools or techniques by which the computing-system (1300)may participate within the overall systems or operating environments. Inone embodiment, program modules (1306) may implement interfaces forproviding communication between local computing systems of a dentistand/or a dental laboratory, and services or processes that operate in acloud computing system.

Processes performed in a cloud-based computing system may be used hereinto refer to a process, processes or a portion of a process, that isconducted over a network (1310) (for example, Internet) by dentists ordental laboratories. Cloud computing systems enable multiple users tohave access to computing resources such as networks, servers, storageand databases, applications and services. Multiple computing systems maysimultaneously connect to a cloud computing system, and have access tothe same computing resources, such as computing power, storage, data,and applications comprising instructions for performing at least aportion of the method steps or processes of the flow diagram of FIG. 10.For example, multiple users may simultaneously access scans of aphysical model of a patient's anatomy that are stored on the network andan associated database located within a cloud computing system, orgenerate and/or retrieve a 3D model automatically generated from thescans by the methods described herein. In one embodiment, the cloudcomputing system comprises an elastic computing system where resources,such as computing power, may be automatically added or decreased basedon, for example, the number of simultaneous connections by computingdevices for accessing the resources and methods and processes disclosedherein.

In one embodiment, patient files may be stored on a remote server ratherthan locally on a storage medium. Cloud computing applications may storecopies of data and/or executable programs at remote server devices,allowing users such as dentists or dental laboratories to download oraccess at least some of this data and program logic as needed forperforming at least a portion of the methods described herein by way ofpersonal computers, tablets, handheld devices, and computer-operatedmachinery and devices.

In one embodiment, the cloud computing system may include a number ofcomputing systems and computing devices coupled to or configured to becapable of communicating with components of the cloud. For example, acomputing system (1300), a host system, a scanning system, CAD and a CAMsystem may all be coupled to the cloud computing system. The host may beany type of computing device or transmitter that is configured totransmit data to the cloud such as a computer, a laptop computer, amobile device and the like. Communication links between computingdevices and cloud computing systems may include wired connections, suchas a serial or parallel bus, or wireless links, such as Bluetooth, IEEE802.11 (including amendments thereto), and the like. The system mayfurther include access points by which computing devices may communicatewith the cloud, such as through wireless access points or a wirelessrouter, a base station in a cellular network that provides internetconnectivity, and the like.

In one embodiment, a method for generating a 3D model of a patient'sdentition for use in designing a tooth restoration comprises one or morethe computer-implemented steps of:

a. scanning a plurality of physical models of a patient's dentition;

b. obtaining scan data of a first physical model of an object thatcomprises a set of characteristic curves;

c. obtaining scan data of a second physical model of an object thatcomprises a second set of characteristic curves;

d. encoding the characteristic curves of each scan as strings;

e. generating string alignments between the strings of each scan

f. identifying a transformation for aligning a pair of curvesrepresented by the strings;

g. evaluating and selecting a transformation;

h. applying the transformation to register the scans of the first andsecond physical models;

i. generating a 3D model, and

j. optionally, one or more steps of designing a dental restoration withCAD, and making a dental restoration with CAM.

In one embodiment, the method comprises the steps of scanning thephysical models of the patient's dentition at a location that is remotefrom a location for making the restoration, and thus, may be performedin a cloud computing system. In another embodiment, the method comprisesthe steps of identifying, extracting and encoding the characteristiccurves of each scan, generating string alignments, evaluatingtransformations, and identifying a transformation for registering thescans, where each step may be performed in a cloud computing system. Ina further embodiment, a database for storing scan data, characteristiccurve sets, strings, transformations and 3D models may be stored in acloud computing system.

A system for generating a computer generated 3D model of an object isalso provided that comprises one or more computing devices, andoptionally, at least one of which is configured to operate in a cloudcomputing system, and a plurality of program modules having instructionsthat are executable by the one or more computing devices, that provideinstructions for performing method steps described above. Programmodules suitable for use in this system comprise one or more of: a)scanning one or more physical models of an object; b) obtaining and/orstoring scan data, such as point clouds or meshes, of at least onephysical object; c) extracting a set of characteristic curves from eachpoint cloud or mesh; d) encoding curves from characteristic curve setsas strings; e) generating string alignments between strings of each setof scan data; f) for each string alignment, identifying atransformation; g) evaluating the transformations for each stringalignment; h) selecting a transformation; i) applying the transformationto register scans; j) identifying scanned physical models thatcorrespond to the object; k) generating a 3D model of the object; m)designing a restoration from the 3D model; and j) providingmanufacturing instructions to make a physical restoration by CAMprocesses.

In one embodiment, the system comprises a first computing deviceconfigured to operate in a cloud computing system, and a secondcomputing device connected to the first computing device through aninternet connection. In another embodiment, the second computing devicecomprises a display module for viewing scans of the physical object orthe computer generated 3D model of the object, and optionally, aplurality of the method steps may be performed in a cloud computingsystem via program modules that are stored or run at a location that isremote from the second computing device. In a further embodiment, thesecond computing device comprises a CPU, a memory, and at least oneprogram module to perform at least one of the method steps forgenerating a 3D model, wherein a plurality of the program modules may berun on the second computing device, and only one or a few method stepsare performed in the cloud. In another embodiment, the second computingdevice comprises at least one program module having executableinstructions for retrieving a computer 3D digital model generated fromthe scans, and generating a restoration design proposal, and theprocesses are performed on the second computing device.

In addition to dental applications, the presently disclosed methods mayhave applications in areas other than dentistry. As such, those skilledin the art will appreciate that other arrangements and other elementssuch as machines, interfaces, functions, orders, and groupings offunctions, and the like, can be used. Further, elements described asfunctional elements may be implemented as discrete components or incombination with other components. Various alternatives, modifications,variations, or improvements therein may be subsequently made by thoseskilled in the art which fall within the scope and spirit of theprinciples of the present disclosure.

All patents, patent applications, and published references cited hereinare hereby incorporated by reference in their entirety. It will beappreciated that various of the above-disclosed and other features andfunctions, or alternatives thereof, may be desirably combined into manyother different systems or applications. Various presently unforeseen orunanticipated alternatives, modifications, variations, or improvementstherein may be subsequently made by those skilled in the art which arealso intended to be encompassed by the following claims.

We claim:
 1. A scanner for scanning a physical model of a patient'sdentition to generate a 3D model of the patient's dentition, comprisinga base for supporting the scanner during scanning; a scanner headassembly for scanning the physical model; a support arm comprising afirst end joined to the base, and a second end joined to the scannerhead assembly; and a swing arm pivotally connected to the support armbetween the base and the scanner head assembly for movement about afirst axis, wherein the swing arm comprises a first portion that isconnected to the support arm, and a second portion perpendicular to thefirst portion comprising a turntable for rotation of the physical modelabout a second axis; and a scanning surface on which the physical objectto be scanned is mounted.
 2. The scanner of claim 1, further comprisinga pedestal that is removably attached to the turntable for rotating thephysical model about the second axis, and the scanning surface is anupper surface of the pedestal.
 3. The scanner of claim 1, wherein thesupport arm is inclined between the first end that is joined to the baseand the second end that is joined to the scanner head assembly.
 4. Thescanner of claim 1, wherein the swing arm is pivotally connected to thesupport arm for displacement about the first axis from a restingposition to a second position.
 5. The scanner of claim 1, wherein theswing arm is pivotally connected to the support arm at a distance abovethe scanning surface.
 6. The scanner of claim 1, wherein the first axisis parallel to the scanning surface, and is above the scanning surfacein a swing arm resting position.
 7. The scanner of claim 1, the scannercomprises a space between the scanning surface and the scanner headassembly for placement of an articulated model of a physical model of afull upper jaw and a physical model of a full lower jaw during scanning.8. The scanner of claim 1, wherein the base of the scanner does notextend between a surface on which the scanner is placed and the swingarm in a resting position.
 9. The scanner of claim 1, wherein thescanner head assembly comprises a structured light scanner.
 10. Ascanner for scanning a physical model of a patient's dentition togenerate a 3D model of the physical object, comprising a base forsupporting the scanner on a surface during scanning; a scanner headassembly for scanning the physical model; a support arm comprising afirst end joined to the base, and a second end joined to the scannerhead assembly; and a swing arm pivotally connected on the support armbetween the base and the scanner head assembly for displacement of theswing arm about a first axis, the swing arm comprising a first swing armportion comprising a first end, wherein the swing arm is pivotallyconnected to the support arm at the first end of the swing arm, and asecond swing arm portion, perpendicular to the first portion, comprisinga turntable comprising a scanning surface on which the physical objectto be scanned is mounted, wherein the first end of the swing arm ishigher than the scanning surface relative to the surface on which thescanner is placed.
 11. The scanner of claim 10, wherein the support armis inclined between the base and the scanner head assembly.
 12. Thescanner of claim 10, wherein the base does not extend between thesurface on which the scanner is placed and the swing arm.
 13. Thescanner of claim 10, wherein the scanner head assembly comprises astructured light scanner.
 14. A scanner for scanning a physical objectto obtain data for making a 3D model of the physical object, comprisinga base for supporting the scanner on a surface; a scanner head assemblyto obtain scan data of the physical object; a turntable comprising ascanning surface for mounting the physical object to be scanned; asupport arm comprising a first end connected to the base, a second end,opposite the first end, connected to the scanner head assembly, andwherein the support arm is inclined from the base to the scanner headassembly; a swing arm comprising a first swing arm portion connectingthe swing arm to the support arm between the first and second ends ofthe support arm, a second swing arm portion that is perpendicular to thefirst swing arm portion, and that is between the first swing arm portionand the turntable, wherein the swing arm is pivotally connected to thesupport arm for movement of the swing arm and the turntable about afirst axis.
 15. The scanner of claim 14, wherein the swing arm comprisesa plurality of scanning positions about the first axis.
 16. The scannerof claim 14, wherein the first axis is higher than the scanning surfacein a swing arm resting position.
 17. The scanner of claim 14, whereinthe base does not extend under a lower surface of the second swing armportion in a swing arm resting position.